K=10,T=0.8: <data>for reporting on forums . " ) parser . add _ argument ( ' - c ' , ' - - config ' , action = ' store _ true ' , help = " report the api configs before all else . " ) if command = = p + " ls " or command = = p + " ll " : parser . add _ argument ( ' - l ' , ' - - long ' , action = ' store _ true ' , help = " long listing format [ default : % ( </data>default [ 0 ] [ 1 ] [ 1 ] [ 1 ] [ 0 ] [ 0 ] [ 0 ] [ 0 ] [ 0 ] [ 0 ] [ 0 ] [ 1 ] [ 0 ] [ 0 ] [ 0 ] [ 0 ] [ 0 ] = p + " " " elif command = = p and command = = ' store _ true ' : parser . add _ argument ( ' - - use _ use _ use _ use _ use _ use _ use _ use _ use _ 
K=10,T=0.8: <data>from model _ builders import ( deconv painter , conv image painter , conv regressor , coord conv painter , coord conv image painter , deconv bottleneck painter , upsample conv painter ) from util import make _ standard _ parser , merge _ dict _ append , average _ dict _ values arch _ choices = [ ' deconv _ classification ' , ' deconv _ rendering ' , ' coordconv _ classification ' , ' coordconv _ rendering ' , ' conv _ regressor ' , ' coordconv _ regressor ' , ' conv _ onehot _ image </data>' ] , kernel _ size = 3 , stride = 3 , padding = 3 , stride = 3 , stride = 3 , stride = 1 , padding = 3 , padding = 1 , groups = 3 , groups = 3 , groups = 3 , groups = 3 , groups = 3 , groups = 3 , groups = 3 , groups = 3 , groups = 3 , groups = 3 , groups = groups , groups = groups , groups = groups , groups = groups , groups = groups , groups = groups 
K=10,T=0.8: <data>_ wgs ( * xframe [ i ] ) else : raise exception ( " invalid argument : source . " ) return xframe def save tiff ( r , g , b , gt , file path ) : fname _ out = file path driver = gdal . get driver by name ( ' <UNK> ' ) dset _ output = driver . create ( fname _ out , r . shape [ 1 ] , r . shape [ 0 ] , 3 , gdal . <UNK> _ byte ) dset _ output . set geo transform </data>( dset _ output ) dset _ output = driver . create ( dset _ output , r . shape [ 0 ] , 3 , 3 , 3 , 3 , 3 , 3 , 3 , 3 , 3 , 3 , 3 , 3 , 3 , 3 , 3 , 3 , 3 , 3 , 3 , 3 , 3 , 3 , 3 , 3 , 3 , 3 , 3 , 3 , 3 , 3 , 3 , 3 , 3 , 3 , 3 , 3 , 3 , 3 , 
K=10,T=0.8: <data>tr _ loss + = loss . item ( ) if ( step + 1 ) % args . gradient _ accumulation _ steps = = 0 : if args . fp 1 6 : torch . nn . utils . clip _ grad _ norm _ ( amp . master _ params ( optimizer ) , args . max _ grad _ norm ) else : torch . nn . utils . clip _ grad _ norm _ ( model . parameters ( ) , args . max _ grad _ norm ) optimizer . step ( ) scheduler </data>. step ( ) if ( epoch < args . gradient _ accumulation _ steps ) : epoch = args . train _ batch _ time + = loss . item ( ) if ( ( epoch + 1 , epoch + 1 ) / args . gradient _ accumulation _ steps ) > = args . gradient _ accumulation _ steps : model . eval ( ) if ( epoch % args . gradient _ accumulation _ steps = = 0 ) : model . eval ( ) else : model . eval ( ) if ( epoch + 
K=10,T=0.8: <data>json . dumps ( trade , indent = 2 ) ) print ( " conflicted _ pending _ trades " ) print ( json . dumps ( conflicted _ pending _ trades , indent = 2 ) ) sys . exit ( 0 ) def calculate _ exposure ( self , trade ) : def correlated ( self , signal ) : return false def calculate _ stop _ price ( self , signal ) : if signal [ ' stop _ price ' ] is not none : return signal [ ' stop _ price ' ] else : </data>return signal [ ' stop _ price ' ] def calculate _ min _ price _ price ( self , signal ) : return false return signal [ ' stop _ price _ price ' ] def calculate _ order ( self , signal ) : return self . max _ price _ price _ price _ price _ price _ price _ price _ price _ price _ price _ price _ price _ price _ price _ price _ price _ price _ price _ price _ price _ price _ price _ price _ price _ price 
K=10,T=0.8: <data>) = = 1 : print ( ' throughput ( in cycles per iteration ) : { : . 2 f } ' . format ( next ( iter ( tpdict ) ) ) ) else : print ( ' throughput ( in cycles per iteration ) : { : . 2 f } - { : . 2 f } \ n ' . format ( min ( tpdict ) , max ( tpdict ) ) ) for tp , al list in sorted ( tpdict . items ( ) ) : print ( ' - { : . </data>2 f } - { : . 2 f } ' . format ( len ( al ) ) ) ) ) print ( ' - { : . 2 f } - { : . 2 f } ' . format ( sum ( al ) , sum ( al ) ) ) ) print ( ' - { : . 2 f } - { : . 2 f } - { : . 2 f } - { : . 2 f } - { : . 2 f } - { : . 2 f } 
K=10,T=0.8: <data>. tx power = = 0 : self . i beacon range = - 1 return try : ratio _ db = float ( self . tx power - self . rssi ) if ratio _ db < 0 . 0 : self . i beacon range = 0 . 0 return elif ratio _ db < = 1 . 5 : self . i beacon range = 0 . 5 return elif ratio _ db < = 3 . 0 : self . i beacon range = 1 . 0 return dist = 1 0 . 0 * * </data>1 0 . 0 * math . log ( self . i + 1 ) def get _ tx power ( self ) : try : self . i beacon range = 1 0 . 0 * math . log ( self . i + 1 ) except : self . i beacon range = 0 . 2 * math . log ( self . i + 1 ) def get _ rx power ( self ) : try : for i in range ( 1 8 ) : if self . i beacon range = = 0 . 5 
K=10,T=0.8: <data>in filters : if filt ( filename ) : use _ file = false break if use _ file : res . append ( m ) return res def dump _ predictions ( result _ ensemble , submission _ path ) : _ , _ , test _ idx = _ get _ train _ val _ test _ ids ( ) test _ ids = np . where ( test _ idx ) [ 0 ] + 1 preds _ sys = result _ ensemble [ " predictions _ systole " ] [ np . where ( test _ </data>idx ) [ 1 ] > 1 ) ) else : preds _ dia = [ ] if is _ file : if not os . path . exists ( os . path . join ( args . output _ dir , " preds " ) ) : preds _ dia = [ ] if os . path . exists ( os . path . join ( args . output _ dir , " preds " ) ) : preds _ dia = np . where ( test _ idx ) [ 0 ] . astype ( np . int 
K=10,T=0.8: <data>> callable [ [ np . ndarray ] , optional [ np . ndarray ] ] : def scale ( width , height , img ) : w = img . shape [ 1 ] h = img . shape [ 0 ] if width = = w and height = = h : return img img = pil . image . fromarray ( img ) ww = width if width is not none else w hh = height if height is not none else h img = img . resize ( ( ww , hh ) , pil . </data>image . antialias , pad _ to _ pad ( hh ) ) hh = hh / hh hh if height is not none else hh / hh if hh is not none else w hh = h if height is not none else hh if hh ! = hh : hh = hh / hh if hh is not none else hh return hh def <UNK> ( self , width , height , height ) : w = width if width is not none else hh if height is not none else none if width is not none else